Featured Publications
Quasi-experimental methods for pharmacoepidemiology: difference-in-differences and synthetic control methods with case studies for vaccine evaluation
Kennedy-Shaffer L. Quasi-experimental methods for pharmacoepidemiology: difference-in-differences and synthetic control methods with case studies for vaccine evaluation. American Journal Of Epidemiology 2024, 193: 1050-1058. PMID: 38456774, PMCID: PMC11228849, DOI: 10.1093/aje/kwae019.Peer-Reviewed Original ResearchConceptsSynthetic control methodDifference-in-differencesHealth policyCase studyAverage treatment effectQuasi-experimental methodPolicyQuasi-experimental designWeight assumptionPopulation-level effectsTime trendsStudy designSources of evidenceConfounding factorsEvaluation studiesPharmacoepidemiologyTarget estimandAbsence of contaminationStatistical Properties of Stepped Wedge Cluster-Randomized Trials in Infectious Disease Outbreaks
Kennedy-Shaffer L, Lipsitch M. Statistical Properties of Stepped Wedge Cluster-Randomized Trials in Infectious Disease Outbreaks. American Journal Of Epidemiology 2020, 189: 1324-1332. PMID: 32648891, PMCID: PMC7604531, DOI: 10.1093/aje/kwaa141.Peer-Reviewed Original ResearchConceptsWedge trialsParallel-arm cluster-randomized trialsStepped wedge cluster randomized trialStatistical propertiesCluster randomized trialStatistical disadvantageStepped wedge trialIndividual randomizationInfectious disease outbreaksCluster randomized designEvaluation of interventionsEvaluate various designsTrial designDetect intervention effectsWedge designRandomized controlled trialsIntervention effectsEffect estimatesControlled trialsParallel-armEpidemic settingsLogistical factorsAdequate powerInfectious disease incidenceRandomized trialsNovel methods for the analysis of stepped wedge cluster randomized trials
Kennedy‐Shaffer L, de Gruttola V, Lipsitch M. Novel methods for the analysis of stepped wedge cluster randomized trials. Statistics In Medicine 2019, 39: 815-844. PMID: 31876979, PMCID: PMC7247054, DOI: 10.1002/sim.8451.Peer-Reviewed Original ResearchMeSH KeywordsCluster AnalysisCross-Over StudiesHumansRandomized Controlled Trials as TopicResearch DesignConceptsSW-CRTsRobust inference proceduresStepped wedge cluster randomized trialParametric model assumptionsModel assumptionsCluster randomized trialNonparametric analysis methodsTheoretical propertiesInference proceduresNonparametric methodsIncorporating covariatesRestrictive assumptionsAssumptionsEffects modelControl approachFeasibility advantagesSynthetic control approachIncreased powerRandomized trialsIntervention clustersMixed effects modelsEstimationTime trendsInterventionModel-based approach
2021
Power and sample size calculations for cluster randomized trials with binary outcomes when intracluster correlation coefficients vary by treatment arm
Kennedy-Shaffer L, Hughes M. Power and sample size calculations for cluster randomized trials with binary outcomes when intracluster correlation coefficients vary by treatment arm. Clinical Trials 2021, 19: 42-51. PMID: 34879711, PMCID: PMC8883478, DOI: 10.1177/17407745211059845.Peer-Reviewed Original ResearchMeSH KeywordsCluster AnalysisHumansLogistic ModelsRandomized Controlled Trials as TopicResearch DesignSample SizeConceptsWorking correlation structureIntracluster correlation coefficientIntracluster correlation coefficient valuesAsymptotic varianceCorrelation structureClustered binary dataSample size requirementsSample size calculationCluster-level covariatesExchangeable working correlation structureCluster randomized trialModest-sized clustersBinary covariateSize calculationBinary outcomesBinary dataDistribution of cluster sizesFormulaCluster size distributionSize requirementsCovariatesEquationsSample sizeCluster sizeRandomized trialsHow to detect and reduce potential sources of biases in studies of SARS-CoV-2 and COVID-19
Accorsi E, Qiu X, Rumpler E, Kennedy-Shaffer L, Kahn R, Joshi K, Goldstein E, Stensrud M, Niehus R, Cevik M, Lipsitch M. How to detect and reduce potential sources of biases in studies of SARS-CoV-2 and COVID-19. European Journal Of Epidemiology 2021, 36: 179-196. PMID: 33634345, PMCID: PMC7906244, DOI: 10.1007/s10654-021-00727-7.Peer-Reviewed Original ResearchMeSH KeywordsBiasCOVID-19HumansReproducibility of ResultsResearch DesignSARS-CoV-2Seroepidemiologic StudiesConceptsRisk Factors StudyPublic health scientistsPotential sources of biasBody of literatureSources of biasStudy designFactor studiesHealth scientistsCategories of studiesObservational studyCOVID-19Selection biasPotential biasSecondary attack rateRisk of infectionGeographical areasAttack rateRiskSusceptibility to infectionStudy of COVID-19Cross-sectional seroprevalenceConfoundingCoronavirus diseaseIntervention